Abstract:
Speech recognition is one of the frontiers in
Human Computer Interaction. A number of tools used to achieve
speech recognition are currently available. One of such tools is
Sphinx4 from Carnegie Mellon University (CMU). It has a
recognition engine based on discrete Hidden Markov Model
(dHMM) and a modular structure making it flexible to a diverse
set of requirements. However, most efforts that have been
undertaken using this tool are focused on established dialects
such as English and French. Despite Swahili being a major
spoken language in Africa, literature search indicates that little
research has been undertaken in developing a speech recognition
tool for this dialect. In this paper, we propose an approach to
building a Swahili speech recognizer using Sphinx4 to
demonstrate its adaptability to recognition of spoken Swahili
words. To realize this, we examined the Swahili language
structure and sound synthesis processes. Then, a 40 word Swahili
acoustic model was built based on the observed language and
sound structures using CMU Sphinx train and associate tools.
The developed acoustic model was then tested using sphinx4.
